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An end-to-end image dehazing method based on convolution neural network is presented to solve the problem in which Unmanned Aerial Vehicle (UAV) high-resolution remote sensing images have reduced image sharpness due to haze. First, the original atmospheric scattering model is adapted to get an end-to-end dehazing model. Then, several unknown parameters are unified into one parameter, and the unknown parameter is estimated by using a multiscale convolution neural network. Finally, the parameter estimates are incorporated into the dehazing model to get a haze-free image. For the no reference image dataset, we first train the network using existing datasets, and then the network is trained using a self-built dataset. In this paper, the haze removal effect for different types of unmanned remote sensing images is tested and compared with those of the main dehazing algorithms. The experiments show that the algorithm in this paper has different degrees of improvement regarding its visual effect and objective indicators. INDEX TERMS End to end image dehazing; Deep learning; Multiscale convolutional neural network; Remote sensing image Yufeng Li, PHD, professor and master's supervisor at the School of electronic information engineering, Shenyang University of Aeronautics and Astronautics. He graduated from Northern Traffic University in July 1991, majoring in communication engineering. In March 2002, he graduated from Jilin University, majoring in communication and information system. In

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This paper proposes a method based on information entropy and support vector machine predict chatter in milling, it uses multi-scale permutation entropy and wavelet packet energy as the milling chatter premonition features, we select parameters of these identifying features by experimental analysis, and predict chatter using the SVM which use these two identifying features as its input. The results show that this method can effectively predict the occurrence of milling chatter, correct rate is 95.8%.

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